Boratko, Michael

10 publications

ICML 2025 A Geometric Approach to Personalized Recommendation with Set-Theoretic Constraints Using Box Embeddings Shib Sankar Dasgupta, Michael Boratko, Andrew Mccallum

ICML 2024 A Fresh Take on Stale Embeddings: Improving Dense Retriever Training with Corrector Networks Nicholas Monath, Will Sussman Grathwohl, Michael Boratko, Rob Fergus, Andrew Mccallum, Manzil Zaheer

NeurIPS 2024 Learning Representations for Hierarchies with Minimal Support Benjamin Rozonoyer, Michael Boratko, Dhruvesh Patel, Wenlong Zhao, Shib Dasgupta, Hung Le, Andrew McCallum

AAAI 2022 An Evaluative Measure of Clustering Methods Incorporating Hyperparameter Sensitivity Siddhartha Mishra, Nicholas Monath, Michael Boratko, Ariel Kobren, Andrew McCallum

ICLR 2022 Modeling Label Space Interactions in Multi-Label Classification Using Box Embeddings Dhruvesh Patel, Pavitra Dangati, Jay-Yoon Lee, Michael Boratko, Andrew McCallum

NeurIPS 2022 Modeling Transitivity and Cyclicity in Directed Graphs via Binary Code Box Embeddings Dongxu Zhang, Michael Boratko, Cameron Musco, Andrew McCallum

NeurIPS 2021 Capacity and Bias of Learned Geometric Embeddings for Directed Graphs Michael Boratko, Dongxu Zhang, Nicholas Monath, Luke Vilnis, Kenneth L Clarkson, Andrew McCallum

UAI 2021 Min/max Stability and Box Distributions Michael Boratko, Javier Burroni, Shib Sankar Dasgupta, Andrew McCallum

NeurIPS 2020 Improving Local Identifiability in Probabilistic Box Embeddings Shib Dasgupta, Michael Boratko, Dongxu Zhang, Luke Vilnis, Xiang Li, Andrew McCallum

ICLR 2019 Smoothing the Geometry of Probabilistic Box Embeddings Xiang Li, Luke Vilnis, Dongxu Zhang, Michael Boratko, Andrew McCallum